Computational stochastic approaches (Monte Carlo methods) based on random sampling are becoming extremely important research tools not only in their "traditional" fields such as physics, chemistry or applied mathematics but also in social sciences and, recently, in various branches of industry. An indication of importance is, for example, the fact that Monte Carlo calculations consume about one half of the supercomputer cycles. One of the indispensable and important ingredients for reliable and statistically sound calculations is the source of pseudo random numbers. SPRNG provides a scalable package for parallel pseudo random number generation which will be easy to use on a variety of architectures, especially in large-scale parallel Monte Carlo applications.
SPRNG 1.0 provides the user the various SPRNG random number generators each in its own library. For most users this is acceptable, as one rarely uses more than one type of generator in a single program. However, if the user desires this added flexibility, SPRNG 2.0 provides it. In all other respects, SPRNG 1.0 and SPRNG 2.0 are identical.